Journal ArticleDOI
The detection of fault-prone programs
TLDR
A principal-components procedure was employed to reduce simple multicollinear complexity metrics to uncorrelated measures on orthogonal complexity domains to classify programs into alternate groups, depending on the metric values of the program.Abstract:
The use of the statistical technique of discriminant analysis as a tool for the detection of fault-prone programs is explored. A principal-components procedure was employed to reduce simple multicollinear complexity metrics to uncorrelated measures on orthogonal complexity domains. These uncorrelated measures were then used to classify programs into alternate groups, depending on the metric values of the program. The criterion variable for group determination was a quality measure of faults or changes made to the programs. The discriminant analysis was conducted on two distinct data sets from large commercial systems. The basic discriminant model was constructed from deliberately biased data to magnify differences in metric values between the discriminant groups. The technique was successful in classifying programs with a relatively low error rate. While the use of linear regression models has produced models of limited value, this procedure shows great promise for use in the detection of program modules with potential for faults. >read more
Citations
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Journal ArticleDOI
A validation of object-oriented design metrics as quality indicators
TL;DR: Several of Chidamber and Kemerer's OO metrics appear to be useful to predict class fault-proneness during the early phases of the life-cycle and are better predictors than "traditional" code metrics, which can only be collected at a later phase of the software development processes.
Journal ArticleDOI
Prioritizing test cases for regression testing
TL;DR: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal as discussed by the authors, such as rate of fault detection, a measure of how quickly faults are detected within the testing process.
Journal ArticleDOI
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
TL;DR: A framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository, showing an appealing degree of predictive accuracy, which supports the view that metric-based classification is useful.
Journal ArticleDOI
A critique of software defect prediction models
Norman Fenton,Martin Neil +1 more
TL;DR: H holistic models for software defect prediction, using Bayesian belief networks, are recommended as alternative approaches to the single-issue models used at present and research into a theory of "software decomposition" is argued for.
Journal ArticleDOI
A Survey on Software Fault Localization
TL;DR: A comprehensive overview of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way is provided.
References
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Book
A complexity measure
TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.
Journal ArticleDOI
Software errors and complexity: an empirical investigation0
TL;DR: An analysis of the distributions and relationships derived from the change data collected during development of a medium-scale software project produces some surprising insights into the factors influencing software development.
Book
Software errors and complexity: an empirical investigation
Abstract: The distributions and relationships derived from the change data collected during the development of a medium scale satellite software project show that meaningful results can be obtained which allow an insight into software traits and the environment in which it is developed. Modified and new modules were shown to behave similarly. An abstract classification scheme for errors which allows a better understanding of the overall traits of a software project is also shown. Finally, various size and complexity metrics are examined with respect to errors detected within the software yielding some interesting results.